Network meta-analysis on the effects of finerenone versus SGLT2 inhibitors and GLP-1 receptor agonists on cardiovascular and renal outcomes in patients with type 2 diabetes mellitus and chronic kidney disease.

Objective: To evaluate the cardiovascular and renal benefits of finerenone, sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagonlike peptide-1 receptor agonists (GLP-1 RA) in patients with Type 2 Diabetes Mellitus (T2DM) and chronic kidney disease (CKD) with network meta-analysis.

Methods: Systematic literature searches were conducted of PubMed, Cochrane Library, Web of Science, Medline and Embase covering January 1, 2000 to December 30, 2021. Randomized control trials (RCTs) comparing finerenone, SGLT-2i and GLP-1 RA in diabetics with CKD were selected.

Treatment options of traditional Chinese patent medicines for dyslipidemia in patients with prediabetes: A systematic review and network meta-analysis.

To compare the clinical efficacy and safety of SIX Traditional Chinese Patent Medicines (TCPM) recommended by guidelines in improving lipids for patients with prediabetes by network meta-analysis. Randomized controlled trials of 6 TCPM in the treatment of prediabetes were searched systematically in various databases. After extracting effective data, the risk of bias was assessed using Review Manager 5.

Modifications decrease hepatic steatosis in Taiwanese with metabolic-associated fatty liver disease.

Metabolic-associated fatty liver disease (MAFLD) is a growing global problem associated with increasing obesity prevalence. Lifestyle modifications are currently recommended, including weight reduction, exercise, and diet control. This study evaluated the short-term effect of lifestyle modifications on transient elastography (TE) values in an obese population with MAFLD.

Context-driven, prescription-based personal activity classification: methodology, architecture, and end-to-end implementation.

Enabling large-scale monitoring and classification of a range of motion activities is of primary importance due to the need by healthcare and fitness professionals to monitor exercises for quality and compliance. Past work has not fully addressed the unique challenges that arise from scaling. This paper presents a novel end-to-end system solution to some of these challenges.

Estimation of accelerometer orientation for activity recognition.

Tri-axial accelerometers have been widely used for human activity recognition and classification. A main challenge in accelerometer-based activity recognition is the system dependence on the orientation of the accelerometer. This paper presents an approach for overcoming this challenge by calibrating the accelerometer orientation using pre-defined activities alongside automated correction algorithms.

A universal hybrid decision tree classifier design for human activity classification.

A system that reliably classifies daily life activities can contribute to more effective and economical treatments for patients with chronic conditions or undergoing rehabilitative therapy. We propose a universal hybrid decision tree classifier for this purpose. The tree classifier can flexibly implement different decision rules at its internal nodes, and can be adapted from a population-based model when supplemented by training data for individuals.